Thomas Pabst

10.6k total citations · 2 hit papers
180 papers, 4.3k citations indexed

About

Thomas Pabst is a scholar working on Hematology, Oncology and Molecular Biology. According to data from OpenAlex, Thomas Pabst has authored 180 papers receiving a total of 4.3k indexed citations (citations by other indexed papers that have themselves been cited), including 107 papers in Hematology, 78 papers in Oncology and 66 papers in Molecular Biology. Recurrent topics in Thomas Pabst's work include Acute Myeloid Leukemia Research (69 papers), Multiple Myeloma Research and Treatments (41 papers) and Lymphoma Diagnosis and Treatment (31 papers). Thomas Pabst is often cited by papers focused on Acute Myeloid Leukemia Research (69 papers), Multiple Myeloma Research and Treatments (41 papers) and Lymphoma Diagnosis and Treatment (31 papers). Thomas Pabst collaborates with scholars based in Switzerland, Germany and Netherlands. Thomas Pabst's co-authors include Beatrice U. Mueller, Ulrike Bacher, Gert J. Ossenkoppele, Bob Löwenberg, Katja Seipel, Bart J. Biemond, Johan Maertens, Edo Vellenga, Leo F. Verdonck and Carlos Graux and has published in prestigious journals such as New England Journal of Medicine, Proceedings of the National Academy of Sciences and Journal of Clinical Oncology.

In The Last Decade

Thomas Pabst

166 papers receiving 4.2k citations

Hit Papers

High-Dose Daunorubicin in... 2009 2026 2014 2020 2009 2022 100 200 300 400 500

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Thomas Pabst 2.5k 1.6k 1.2k 738 713 180 4.3k
Stephen H. Petersdorf 2.6k 1.0× 1.2k 0.7× 1.7k 1.4× 988 1.3× 769 1.1× 78 4.7k
Yago Nieto 1.9k 0.7× 859 0.5× 1.7k 1.5× 594 0.8× 644 0.9× 242 4.0k
Rena Buckstein 2.5k 1.0× 1.6k 1.0× 1.5k 1.2× 1.4k 1.9× 1.6k 2.2× 191 5.1k
G.J. Ossenkoppele 1.6k 0.6× 783 0.5× 1.1k 1.0× 812 1.1× 677 0.9× 84 3.3k
Keith Stockerl‐Goldstein 3.3k 1.3× 1.7k 1.0× 1.8k 1.5× 588 0.8× 671 0.9× 200 5.2k
Stefan Knop 1.9k 0.8× 1.9k 1.2× 2.1k 1.8× 567 0.8× 396 0.6× 152 4.4k
Carlos Graux 2.2k 0.9× 1.1k 0.7× 980 0.8× 392 0.5× 581 0.8× 91 3.6k
Martin Höglund 3.4k 1.4× 1.5k 0.9× 1.2k 1.0× 283 0.4× 1.6k 2.2× 134 5.1k
Ellin Berman 2.2k 0.9× 1.2k 0.8× 1.2k 1.0× 302 0.4× 992 1.4× 99 3.8k
A. Keith Stewart 1.8k 0.7× 2.4k 1.4× 1.4k 1.2× 483 0.7× 463 0.6× 100 3.9k

Countries citing papers authored by Thomas Pabst

Since Specialization
Citations

This map shows the geographic impact of Thomas Pabst's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Thomas Pabst with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Pabst more than expected).

Fields of papers citing papers by Thomas Pabst

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas Pabst. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Thomas Pabst. The network helps show where Thomas Pabst may publish in the future.

Co-authorship network of co-authors of Thomas Pabst

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Pabst. A scholar is included among the top collaborators of Thomas Pabst based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Thomas Pabst. Thomas Pabst is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Jutzi, Jonas S., Jason A. Wampfler, Michèle J. Hoffmann, et al.. (2025). CAR T‐Cell Therapies for Patients With Relapsed and Refractory Aggressive Lymphomas: Real‐World Experiences From a Single Center on the Use of Radiotherapy. Hematological Oncology. 43(5). e70124–e70124.
2.
Seipel, Katja, et al.. (2025). Clinical Impact of LAG3 Single-Nucleotide Polymorphism in DLBCL Treated with CAR-T Cell Therapy. International Journal of Molecular Sciences. 26(20). 9905–9905. 1 indexed citations
3.
Seipel, Katja, et al.. (2025). Clinical Impact of CTLA-4 Single-Nucleotide Polymorphism in DLBCL Patients Treated with CAR-T Cell Therapy. Current Oncology. 32(8). 425–425. 3 indexed citations
5.
Seipel, Katja, et al.. (2024). Rising Prevalence of Low-Frequency PPM1D Gene Mutations after Second HDCT in Multiple Myeloma. Current Issues in Molecular Biology. 46(8). 8197–8208. 2 indexed citations
6.
Akhoundova, Dilara, Carlo R. Largiadèr, Katja Seipel, et al.. (2023). Efficacy and Safety of High-Dose Chemotherapy with Treosulfan and Melphalan in Multiple Myeloma. Cancers. 15(10). 2699–2699. 7 indexed citations
7.
Bacher, Ulrike, Katja Seipel, Naomi Porret, et al.. (2023). CAR T-Cell Persistence Correlates with Improved Outcome in Patients with B-Cell Lymphoma. International Journal of Molecular Sciences. 24(6). 5688–5688. 40 indexed citations
8.
Bacher, Ulrike, Evgenii Shumilov, & Thomas Pabst. (2023). Increasing insight in the value of repetitive COVID‐19 vaccination in patients with haematological malignancies in the Omicron era. British Journal of Haematology. 204(2). 386–388.
9.
Akhoundova, Dilara, et al.. (2023). Stem Cell Mobilization with Ixazomib and G-CSF in Patients with Multiple Myeloma. Cancers. 15(2). 430–430. 4 indexed citations
11.
Seipel, Katja, et al.. (2022). Rationale for Combining the BCL2 Inhibitor Venetoclax with the PI3K Inhibitor Bimiralisib in the Treatment of IDH2- and FLT3-Mutated Acute Myeloid Leukemia. International Journal of Molecular Sciences. 23(20). 12587–12587. 4 indexed citations
12.
Rausch, Christian, Ulrike Bacher, Manuela Rabaglio, et al.. (2022). Combining BeEAM with Brentuximab Vedotin for High-Dose Therapy in CD30 Positive Lymphomas before Autologous Transplantation—A Phase I Study. Journal of Clinical Medicine. 11(18). 5378–5378. 2 indexed citations
13.
Novak, Urban, et al.. (2021). Transformed Lymphoma Is Associated with a Favorable Response to CAR-T-Cell Treatment in DLBCL Patients. Cancers. 13(23). 6073–6073. 18 indexed citations
14.
Idle, Jeffrey R., Katja Seipel, Ulrike Bacher, Thomas Pabst, & Diren Beyoğlu. (2020). (2R,3S)-Dihydroxybutanoic Acid Synthesis as a Novel Metabolic Function of Mutant Isocitrate Dehydrogenase 1 and 2 in Acute Myeloid Leukemia. Cancers. 12(10). 2842–2842. 9 indexed citations
15.
Flach, Johanna, Evgenii Shumilov, Gertrud Wiedemann, et al.. (2020). Clinical potential of introducing next‐generation sequencing in patients at relapse of acute myeloid leukemia. Hematological Oncology. 38(4). 425–431. 10 indexed citations
16.
Bacher, Ulrike, et al.. (2020). Prophylactic corticosteroid use prevents engraftment syndrome in patients after autologous stem cell transplantation. Hematological Oncology. 39(1). 97–104. 10 indexed citations
17.
Berger, Martin D., et al.. (2019). Simple acute phase protein score to predict long‐term survival in patients with acute myeloid leukemia. Hematological Oncology. 38(1). 74–81. 14 indexed citations
18.
Mueller, Beatrice U., Katja Seipel, Ulrike Bacher, & Thomas Pabst. (2018). Autologous Transplantation for Older Adults with AML. Cancers. 10(9). 340–340. 18 indexed citations
19.
Seipel, Katja, et al.. (2015). Inactivation of the p53–KLF4–CEBPA Axis in Acute Myeloid Leukemia. Clinical Cancer Research. 22(3). 746–756. 41 indexed citations
20.
Schardt, Julian, Beatrice U. Mueller, & Thomas Pabst. (2011). Activation of the Unfolded Protein Response in Human Acute Myeloid Leukemia. Methods in enzymology on CD-ROM/Methods in enzymology. 489. 227–243. 21 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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